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TensorFlow Official Models

The TensorFlow official models are a collection of models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read.

These models are used as end-to-end tests, ensuring that the models run with the same or improved speed and performance with each new TensorFlow build.

Tensorflow releases

The master branch of the models are in development with TensorFlow 2.x, and they target the nightly binaries built from the master branch of TensorFlow. You may start from installing with pip:

pip3 install tf-nightly

Stable versions of the official models targeting releases of TensorFlow are available as tagged branches or downloadable releases. Model repository version numbers match the target TensorFlow release, such that release v2.1.0 are compatible with TensorFlow v2.1.0.

If you are on a version of TensorFlow earlier than 1.4, please update your installation.

Requirements

Please follow the below steps before running models in this repo:

  1. TensorFlow nightly binaries

  2. If users would like to clone this repo but do not care about change history, please consider:

export repo_version="master"
git clone -b ${repo_version} https://github.com/tensorflow/models.git --depth=1
  1. Add the top-level /models folder to the Python path with the command:
export PYTHONPATH=$PYTHONPATH:/path/to/models

Using Colab:

import os
os.environ['PYTHONPATH'] += ":/path/to/models"
  1. Install dependencies:
pip3 install --user -r official/requirements.txt

To make Official Models easier to use, we are planning to create a pip installable Official Models package. This is being tracked in #917.

Available models

NOTE: For Officially Supported TPU models please check README-TPU.

NOTE: Please make sure to follow the steps in the Requirements section.

Natural Language Processing

  • albert: A Lite BERT for Self-supervised Learning of Language Representations.
  • bert: A powerful pre-trained language representation model: BERT, which stands for Bidirectional Encoder Representations from Transformers.
  • transformer: A transformer model to translate the WMT English to German dataset.
  • xlnet: XLNet: Generalized Autoregressive Pretraining for Language Understanding.

Computer Vision

  • mnist: A basic model to classify digits from the MNIST dataset.
  • resnet: A deep residual network that can be used to classify both CIFAR-10 and ImageNet's dataset of 1000 classes.
  • retinanet: A fast and powerful object detector.

Others

  • ncf: Neural Collaborative Filtering model for recommendation tasks.

Models that will not update to TensorFlow 2.x stay inside R1 directory:

  • boosted_trees: A Gradient Boosted Trees model to classify higgs boson process from HIGGS Data Set.
  • wide_deep: A model that combines a wide model and deep network to classify census income data.

More models to come!

We are in the progress to revamp official model garden with TensorFlow 2.0 and Keras. In the near future, we will bring:

  • State-of-the-art language understanding models: XLNet, GPT2, and more members in Transformer family.
  • Start-of-the-art image classification models: EfficientNet, MnasNet and variants.
  • A set of excellent objection detection models.

If you would like to make any fixes or improvements to the models, please submit a pull request.

New Models

The team is actively working to add new models to the repository. Every model should follow the following guidelines, to uphold the our objectives of readable, usable, and maintainable code.

General guidelines

  • Code should be well documented and tested.
  • Runnable from a blank environment with relative ease.
  • Trainable on: single GPU/CPU (baseline), multiple GPUs, TPU
  • Compatible with Python 3 (using six when being compatible with Python 2 is necessary)
  • Conform to Google Python Style Guide

Implementation guidelines

These guidelines exist so the model implementations are consistent for better readability and maintainability.